Shelf-life estimation from accelerated storage data

Traditionally, deterioration rates at elevated temperatures are extrapolated to lower ones assuming that their temperature dependence obeys the Arrhenius equation, or a similar model. For such methods to succeed, the spoilage kinetics must be known in advance and be completely defined by a single rate constant that must be independent of the food's thermal history. These assumptions may hold for certain foods and model systems, but they cannot be universally applicable. In a proposed alternative approach, no kinetics is assumed at all and it is taken for granted that the food's thermal history can and does affect the deterioration pattern. Consequently, the isothermal chemical degradation or microbial growth data are described in terms of empirical models that have the needed number of temperature dependent parameters, usually two or three. The temperature dependence of these is also described by ad hoc empirical models that are used for the extrapolation to lower temperatures. This enables the estimation of not only the ‘rate constant’ but also the whole deterioration curve. The concept is demonstrated with the degradation of vitamin C in frozen spinach and the growth of a bacterium and yeast at different temperatures. An example of lowering the salt concentration as a way of accelerating bacterial growth is also given.The quality of the predictions largely depends on the quality of the data gathered under the accelerating conditions and the proximity of these to the conditions of normal handling and storage. At least theoretically, the proposed method predictions can be extended to non-isothermal storage conditions even when the rate is a function not only of the momentary temperature but also of the food's previous thermal history. In principle, the same methodology can be used to predict quality improvement in products undergoing aging.